{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PudFGcqEcaJk",
        "outputId": "4056ca24-0db3-4cf8-c3ac-11b511828a16"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.5/42.5 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.3/67.3 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.2/48.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m240.2/240.2 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.7/6.7 MB\u001b[0m \u001b[31m61.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m545.9/545.9 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m48.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.4/211.4 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m628.3/628.3 kB\u001b[0m \u001b[31m29.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.4/71.4 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m32.6/32.6 MB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.0/65.0 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.9/55.9 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.7/118.7 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m177.4/177.4 kB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.6/5.6 MB\u001b[0m \u001b[31m68.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m69.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.6/57.6 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m756.0/756.0 kB\u001b[0m \u001b[31m35.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.0/236.0 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.2/16.2 MB\u001b[0m \u001b[31m44.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m231.8/231.8 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m284.2/284.2 kB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m253.9/253.9 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.9/94.9 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.6/131.6 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m48.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.1/45.1 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m45.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m311.8/311.8 kB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m106.5/106.5 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m101.6/101.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m58.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.8/76.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m35.7/35.7 MB\u001b[0m \u001b[31m9.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.0/302.0 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.9/2.9 MB\u001b[0m \u001b[31m50.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m40.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.3/62.3 kB\u001b[0m \u001b[31m3.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m52.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m415.1/415.1 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m209.2/209.2 kB\u001b[0m \u001b[31m11.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m319.7/319.7 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m50.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m508.0/508.0 kB\u001b[0m \u001b[31m19.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m306.7/306.7 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.0/72.0 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.0/4.0 MB\u001b[0m \u001b[31m47.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m452.6/452.6 kB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.0/46.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m46.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.9/50.9 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for pypika (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "transformers 4.48.3 requires tokenizers<0.22,>=0.21, but you have tokenizers 0.20.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q -U crewai crewai-tools"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import userdata\n",
        "\n",
        "os.environ[\"GEMINI_API_KEY\"] = userdata.get('GOOGLE_API_KEY')\n",
        "os.environ[\"MODEL\"] = \"gemini/gemini-2.0-flash\"\n",
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n"
      ],
      "metadata": {
        "id": "xQMeQuoKciS_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"OPENAI_API_KEY\"] = userdata.get('OPENAI_API_KEY')\n"
      ],
      "metadata": {
        "id": "BkSGW2GsiXCB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"MODEL\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "gBhSEqq-c1y8",
        "outputId": "113a1439-5f5a-4349-8088-c858193b4f3a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'gemini/gemini-2.0-flash'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import nest_asyncio\n",
        "\n",
        "nest_asyncio.apply()"
      ],
      "metadata": {
        "id": "v8RYxbywc-kF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!crewai create crew pr1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1WF-2AOEdJfc",
        "outputId": "68a8a6c9-af67-4f2d-e4ee-303b9e5c9493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m\u001b[1mCreating folder pr1...\u001b[0m\n",
            "\u001b[36mCache expired or not found. Fetching provider data from the web...\u001b[0m\n",
            "\r\u001b[?25lDownloading  [------------------------------------]  0/16712\r\u001b[?25lDownloading  [#################-------------------]  8192/16712\r\u001b[?25lDownloading  [###################################-]  16384/16712\r\u001b[?25lDownloading  [####################################]  24576/16712\r\u001b[?25lDownloading  [####################################]  32768/16712\r\u001b[?25lDownloading  [####################################]  40960/16712\r\u001b[?25lDownloading  [####################################]  49152/16712\r\u001b[?25lDownloading  [####################################]  57344/16712\r\u001b[?25lDownloading  [####################################]  65536/16712\r\u001b[?25lDownloading  [####################################]  73728/16712\r\u001b[?25lDownloading  [####################################]  81920/16712\r\u001b[?25lDownloading  [####################################]  90112/16712\r\u001b[?25lDownloading  [####################################]  98304/16712\r\u001b[?25lDownloading  [####################################]  106496/16712\r\u001b[?25lDownloading  [####################################]  114688/16712\r\u001b[?25lDownloading  [####################################]  122880/16712\r\u001b[?25lDownloading  [####################################]  131072/16712\r\u001b[?25lDownloading  [####################################]  139264/16712\r\u001b[?25lDownloading  [####################################]  147456/16712\r\u001b[?25lDownloading  [####################################]  155648/16712\r\u001b[?25lDownloading  [####################################]  163840/16712\r\u001b[?25lDownloading  [####################################]  172032/16712\r\u001b[?25lDownloading  [####################################]  180224/16712\r\u001b[?25lDownloading  [####################################]  188416/16712\r\u001b[?25lDownloading  [####################################]  196608/16712\r\u001b[?25lDownloading  [####################################]  204800/16712\r\u001b[?25lDownloading  [####################################]  212992/16712\r\u001b[?25lDownloading  [####################################]  221184/16712\r\u001b[?25lDownloading  [####################################]  229376/16712\r\u001b[?25lDownloading  [####################################]  237568/16712\r\u001b[?25lDownloading  [####################################]  245760/16712\r\u001b[?25lDownloading  [####################################]  253952/16712\r\u001b[?25lDownloading  [####################################]  262144/16712\r\u001b[?25lDownloading  [####################################]  270336/16712\r\u001b[?25lDownloading  [####################################]  278528/16712\r\u001b[?25lDownloading  [####################################]  286720/16712\r\u001b[?25lDownloading  [####################################]  294912/16712\r\u001b[?25lDownloading  [####################################]  303104/16712\r\u001b[?25lDownloading  [####################################]  311296/16712\r\u001b[?25lDownloading  [####################################]  319488/16712\r\u001b[?25lDownloading  [####################################]  327680/16712\r\u001b[?25lDownloading  [####################################]  335872/16712\r\u001b[?25lDownloading  [####################################]  344064/16712\r\u001b[?25lDownloading  [####################################]  347222/16712\u001b[?25h\n",
            "\u001b[36mSelect a provider to set up:\u001b[0m\n",
            "\u001b[36m1. openai\u001b[0m\n",
            "\u001b[36m2. anthropic\u001b[0m\n",
            "\u001b[36m3. gemini\u001b[0m\n",
            "\u001b[36m4. nvidia_nim\u001b[0m\n",
            "\u001b[36m5. groq\u001b[0m\n",
            "\u001b[36m6. ollama\u001b[0m\n",
            "\u001b[36m7. watson\u001b[0m\n",
            "\u001b[36m8. bedrock\u001b[0m\n",
            "\u001b[36m9. azure\u001b[0m\n",
            "\u001b[36m10. cerebras\u001b[0m\n",
            "\u001b[36m11. sambanova\u001b[0m\n",
            "\u001b[36m12. other\u001b[0m\n",
            "\u001b[36mq. Quit\u001b[0m\n",
            "Enter the number of your choice or 'q' to quit: 3\n",
            "\u001b[36mSelect a model to use for Gemini:\u001b[0m\n",
            "\u001b[36m1. gemini/gemini-1.5-flash\u001b[0m\n",
            "\u001b[36m2. gemini/gemini-1.5-pro\u001b[0m\n",
            "\u001b[36m3. gemini/gemini-gemma-2-9b-it\u001b[0m\n",
            "\u001b[36m4. gemini/gemini-gemma-2-27b-it\u001b[0m\n",
            "\u001b[36mq. Quit\u001b[0m\n",
            "Enter the number of your choice or 'q' to quit: 1\n",
            "Enter your GEMINI API key (press Enter to skip): AIzaSyAqiSR3-94buWtVyGxcm8gdbk1zOP_1I0o\n",
            "\u001b[32mAPI keys and model saved to .env file\u001b[0m\n",
            "\u001b[32mSelected model: gemini/gemini-1.5-flash\u001b[0m\n",
            "\u001b[32m  - Created pr1/.gitignore\u001b[0m\n",
            "\u001b[32m  - Created pr1/pyproject.toml\u001b[0m\n",
            "\u001b[32m  - Created pr1/README.md\u001b[0m\n",
            "\u001b[32m  - Created pr1/knowledge/user_preference.txt\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/__init__.py\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/main.py\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/crew.py\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/tools/custom_tool.py\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/tools/__init__.py\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/config/agents.yaml\u001b[0m\n",
            "\u001b[32m  - Created pr1/src/pr1/config/tasks.yaml\u001b[0m\n",
            "\u001b[32m\u001b[1mCrew pr1 created successfully!\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "pyrI4iicdwCa",
        "outputId": "275b9e89-9373-4f95-eef9-d592d768e5ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd pr1"
      ],
      "metadata": {
        "id": "SgPPamYbd15w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FJWOv-0od5Ll",
        "outputId": "86b2e020-7562-4449-9368-def1025c63f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pr1  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!crewai test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "Z5wL80AQd9x_",
        "outputId": "b0445623-d454-440a-d25e-5841c8cc80e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing the crew for 3 iterations with model gpt-4o-mini\n",
            "test: missing argument after ‘gpt-4o-mini’\n",
            "An error occurred while testing the crew: Command '['uv', 'run', 'test', '3', 'gpt-4o-mini']' returned non-zero exit status 2.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd pr1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIxjK_nJimJB",
        "outputId": "cea58da3-8801-439b-c120-247fce1be02a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/pr1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kobMsuQrio_F",
        "outputId": "50c4d36f-9737-4b1f-e678-14c985d26a35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "knowledge  pyproject.toml  README.md  src  tests\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing your Crews"
      ],
      "metadata": {
        "id": "HipwAxg6lgBy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Open /content/pr1/src/pr1/main.py and pass current_year in the test and train functions.\n",
        "\n",
        "You can copy the inputs from KickOff.\n",
        "\n",
        "After updating your test function will be like\n",
        "\n",
        "```python\n",
        "\n",
        "def test():\n",
        "    \"\"\"\n",
        "    Test the crew execution and returns the results.\n",
        "    \"\"\"\n",
        "    inputs = {\n",
        "        \"topic\": \"AI LLMs\",\n",
        "        'current_year': str(datetime.now().year)\n",
        "    }\n",
        "    try:\n",
        "        Pr1().crew().test(\n",
        "          n_iterations=int(sys.argv[1]),\n",
        "        openai_model_name=sys.argv[2],\n",
        "        inputs=inputs)\n",
        "\n",
        "    except Exception as e:\n",
        "        raise Exception(f\"An error occurred while testing the crew: {e}\")\n",
        "\n",
        "```"
      ],
      "metadata": {
        "id": "SiZrwEwzjUm3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!crewai test -n 2 -m gpt-4o-mini"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7L4g9wmicsF",
        "outputId": "bb5c48ab-ea5f-4af9-9ab8-a2c5e4f4e2cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing the crew for 2 iterations with model gpt-4o\n",
            "WARNING:opentelemetry.trace:Overriding of current TracerProvider is not allowed\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Senior Data Researcher\u001b[00m\n",
            "\u001b[95m## Task:\u001b[00m \u001b[92mConduct a thorough research about AI LLMs Make sure you find any interesting and relevant information given the current year is 2025.\n",
            "\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Senior Data Researcher\u001b[00m\n",
            "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
            "Here are 10 bullet points outlining the most relevant information about AI LLMs in 2025:\n",
            "\n",
            "*   **Widespread Adoption of Multimodal LLMs:** LLMs are no longer confined to text. By 2025, multimodal models that seamlessly integrate and process text, images, audio, and video are commonplace. This has revolutionized fields like education (interactive learning experiences), healthcare (AI-assisted diagnosis using medical images and reports), and entertainment (personalized content creation).\n",
            "\n",
            "*   **Edge-Based LLM Processing:** The rise of powerful edge computing devices has enabled LLMs to run directly on smartphones, autonomous vehicles, and IoT devices. This reduces latency, enhances privacy (data doesn't need to be sent to the cloud), and enables offline functionality. Personalized AI assistants that adapt to user behavior without constant internet connectivity are standard.\n",
            "\n",
            "*   **LLM-Powered Code Generation and Debugging:** AI-driven code generation tools powered by LLMs have dramatically accelerated software development. These tools can translate natural language descriptions into functional code, automate debugging, and even refactor legacy systems. The role of human programmers has shifted towards architecture design and complex problem-solving.\n",
            "\n",
            "*   **Advanced Personalization and Customization:** LLMs can be fine-tuned to individual user preferences and needs with unprecedented accuracy. AI companions that understand nuanced emotional states, provide personalized advice, and adapt to changing circumstances are widely available. This personalization extends to content creation, where LLMs can generate stories, music, and art tailored to specific tastes.\n",
            "\n",
            "*   **LLMs in Scientific Discovery:** Researchers are leveraging LLMs to accelerate scientific breakthroughs. These models can analyze vast datasets, identify patterns, and generate hypotheses in fields like drug discovery, materials science, and climate modeling. AI-driven scientific simulations are becoming increasingly sophisticated.\n",
            "\n",
            "*   **Ethical Considerations and Robustness Testing:** Concerns about bias, misinformation, and misuse of LLMs have led to stricter regulations and more robust testing methodologies. Adversarial attacks and bias detection tools are used extensively to ensure fairness, transparency, and safety. Explainable AI (XAI) techniques provide insights into LLM decision-making processes.\n",
            "\n",
            "*   **Integration with Quantum Computing (Early Stages):** While still in its early phases, the integration of quantum computing with LLMs is showing promising results. Quantum-enhanced LLMs can process complex data more efficiently and potentially solve problems that are intractable for classical computers. This is particularly relevant in areas like cryptography and financial modeling.\n",
            "\n",
            "*   **Democratization of LLM Development:** User-friendly platforms and open-source tools have democratized LLM development, allowing individuals and small organizations to create custom models for specific applications. This has fostered innovation and led to a proliferation of niche AI solutions.\n",
            "\n",
            "*   **LLMs for Enhanced Cybersecurity:** LLMs are being used to proactively identify and mitigate cyber threats. These models can analyze network traffic, detect anomalies, and automate incident response. AI-powered security systems are becoming increasingly sophisticated at adapting to evolving cyberattacks.\n",
            "\n",
            "*   **LLMs Bridging Language Barriers:** Real-time, high-fidelity language translation powered by LLMs has broken down communication barriers across the globe. AI-enabled translation devices and software facilitate seamless interactions in international business, education, and diplomacy. Culturally sensitive translation that takes into account nuances and idioms is now standard.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Reporting Analyst\u001b[00m\n",
            "\u001b[95m## Task:\u001b[00m \u001b[92mReview the context you got and expand each topic into a full section for a report. Make sure the report is detailed and contains any and all relevant information.\n",
            "\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Reporting Analyst\u001b[00m\n",
            "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
            "## AI LLMs in 2025: A Comprehensive Report\n",
            "\n",
            "### 1. Widespread Adoption of Multimodal LLMs\n",
            "\n",
            "By 2025, Large Language Models (LLMs) have transcended their initial focus on text-based processing, achieving widespread adoption as multimodal models. These advanced LLMs possess the capability to seamlessly integrate and process diverse data types, including text, images, audio, and video, thereby revolutionizing various sectors.\n",
            "\n",
            "**Impact on Education:** The education sector has witnessed a transformation through interactive and personalized learning experiences powered by multimodal LLMs. These models can generate dynamic educational content, adapt to individual learning styles, and provide real-time feedback, making learning more engaging and effective. Imagine a virtual tutor that can explain complex scientific concepts using text, diagrams, and interactive simulations, all tailored to the student's understanding level.\n",
            "\n",
            "**Healthcare Revolution:** In healthcare, multimodal LLMs are instrumental in AI-assisted diagnosis. These models can analyze medical images (X-rays, MRIs, CT scans) in conjunction with patient reports and medical history to identify anomalies and assist doctors in making accurate diagnoses. Furthermore, they can analyze audio recordings of patient consultations to detect subtle cues indicating potential health issues. This leads to faster and more accurate diagnoses, improved patient outcomes, and reduced healthcare costs. For example, an LLM could analyze a dermatologist's visual examination of a skin lesion along with the patient's description of symptoms to suggest possible diagnoses and recommend further testing.\n",
            "\n",
            "**Entertainment Industry Transformation:** The entertainment industry has embraced multimodal LLMs for personalized content creation. These models can generate stories, music, and art tailored to specific audience preferences. They can also be used to create realistic virtual characters and environments for video games and movies. For instance, an LLM could generate a unique movie script based on user-specified themes, characters, and plot twists, or compose a personalized musical score based on the listener's emotional state.\n",
            "\n",
            "**Underlying Technology:** This shift towards multimodality is driven by advancements in neural network architectures that enable the fusion of information from different modalities. Techniques like attention mechanisms and transformer networks play a crucial role in aligning and integrating data from various sources.\n",
            "\n",
            "### 2. Edge-Based LLM Processing\n",
            "\n",
            "The proliferation of powerful edge computing devices has enabled LLMs to run directly on smartphones, autonomous vehicles, and IoT devices. This paradigm shift, known as edge-based LLM processing, brings about significant advantages:\n",
            "\n",
            "**Reduced Latency:** By processing data locally, edge-based LLMs eliminate the need to transmit data to the cloud for processing, resulting in significantly reduced latency. This is particularly crucial for applications that require real-time responsiveness, such as autonomous driving and robotics.\n",
            "\n",
            "**Enhanced Privacy:** Edge-based processing enhances user privacy by keeping data on the device, preventing it from being sent to remote servers. This is particularly important for sensitive information, such as personal health data and financial transactions.\n",
            "\n",
            "**Offline Functionality:** Edge-based LLMs enable offline functionality, allowing users to access and utilize AI-powered services even without an internet connection. This is beneficial in areas with limited or unreliable internet access.\n",
            "\n",
            "**Personalized AI Assistants:** Personalized AI assistants that adapt to user behavior without constant internet connectivity are now standard. These assistants can learn user preferences, provide tailored recommendations, and automate tasks, all while respecting user privacy. For example, an AI assistant running on a smartphone can learn a user's daily routine and proactively provide relevant information, such as traffic updates and appointment reminders, without ever sending data to the cloud.\n",
            "\n",
            "**Technical Details:** Edge-based LLM processing is made possible by advancements in model compression and quantization techniques, which reduce the size and computational requirements of LLMs without significantly sacrificing accuracy. Specialized hardware accelerators, such as neural processing units (NPUs), are also crucial for accelerating LLM inference on edge devices.\n",
            "\n",
            "### 3. LLM-Powered Code Generation and Debugging\n",
            "\n",
            "AI-driven code generation tools powered by LLMs have dramatically accelerated software development.\n",
            "\n",
            "**Natural Language to Code:** These tools can translate natural language descriptions into functional code, allowing developers to express their intentions in a more intuitive way. This significantly reduces the time and effort required to write code. For instance, a developer could simply type \"create a function that sorts a list of numbers in ascending order,\" and the LLM would generate the corresponding code in the desired programming language.\n",
            "\n",
            "**Automated Debugging:** LLMs can analyze code and identify potential bugs and vulnerabilities. They can also suggest fixes and provide explanations for the identified issues. This helps developers to write more robust and reliable code.\n",
            "\n",
            "**Legacy System Refactoring:** LLMs can assist in refactoring legacy systems, making them more maintainable and efficient. They can automatically identify and replace outdated code patterns with more modern and optimized alternatives.\n",
            "\n",
            "**Shift in Programmer Roles:** The role of human programmers has shifted towards architecture design and complex problem-solving. Instead of spending time writing boilerplate code, programmers can focus on designing the overall structure of software systems and solving complex algorithmic problems.\n",
            "\n",
            "**Examples:** Numerous platforms and IDEs have integrated LLM-powered code generation and debugging tools. These tools often provide features such as code completion, error highlighting, and automated refactoring.\n",
            "\n",
            "### 4. Advanced Personalization and Customization\n",
            "\n",
            "LLMs can be fine-tuned to individual user preferences and needs with unprecedented accuracy.\n",
            "\n",
            "**AI Companions:** AI companions that understand nuanced emotional states, provide personalized advice, and adapt to changing circumstances are widely available. These companions can provide emotional support, help users manage their stress, and offer guidance on personal and professional matters. For example, an AI companion could detect when a user is feeling stressed and offer calming techniques or suggest engaging in relaxing activities.\n",
            "\n",
            "**Personalized Content Creation:** This personalization extends to content creation, where LLMs can generate stories, music, and art tailored to specific tastes. LLMs can analyze user preferences and generate content that aligns with their interests and emotional needs.\n",
            "\n",
            "**Hyper-Personalization in Marketing and Advertising:** LLMs enable hyper-personalization in marketing and advertising. They can analyze customer data to understand individual needs and preferences and deliver targeted messages and offers. This leads to increased customer engagement and higher conversion rates.\n",
            "\n",
            "**Technical Implementation:** This level of personalization is achieved through techniques such as fine-tuning, transfer learning, and reinforcement learning. LLMs are trained on vast datasets of user data to learn individual preferences and behaviors.\n",
            "\n",
            "### 5. LLMs in Scientific Discovery\n",
            "\n",
            "Researchers are leveraging LLMs to accelerate scientific breakthroughs.\n",
            "\n",
            "**Data Analysis and Pattern Identification:** These models can analyze vast datasets, identify patterns, and generate hypotheses in fields like drug discovery, materials science, and climate modeling. LLMs can sift through massive amounts of scientific literature, experimental data, and simulation results to identify previously unknown relationships and correlations.\n",
            "\n",
            "**Hypothesis Generation:** LLMs can generate novel hypotheses based on the patterns they identify. These hypotheses can then be tested experimentally or through simulations.\n",
            "\n",
            "**AI-Driven Scientific Simulations:** AI-driven scientific simulations are becoming increasingly sophisticated. LLMs can be used to create more accurate and efficient simulations of complex systems, such as the Earth's climate and the human brain.\n",
            "\n",
            "**Examples:** LLMs are being used to accelerate drug discovery by predicting the properties of potential drug candidates and identifying promising targets. They are also being used to design new materials with specific properties.\n",
            "\n",
            "### 6. Ethical Considerations and Robustness Testing\n",
            "\n",
            "Concerns about bias, misinformation, and misuse of LLMs have led to stricter regulations and more robust testing methodologies.\n",
            "\n",
            "**Bias Detection and Mitigation:** Adversarial attacks and bias detection tools are used extensively to ensure fairness, transparency, and safety. These tools help to identify and mitigate biases in LLM training data and model architectures.\n",
            "\n",
            "**Misinformation Detection:** LLMs are being used to detect and combat the spread of misinformation online. These models can analyze text and identify potentially false or misleading information.\n",
            "\n",
            "**Explainable AI (XAI):** Explainable AI (XAI) techniques provide insights into LLM decision-making processes. XAI helps to understand why an LLM made a particular decision, which is crucial for building trust and accountability.\n",
            "\n",
            "**Regulatory Frameworks:** Governments and organizations are developing regulatory frameworks to govern the development and deployment of LLMs. These frameworks aim to ensure that LLMs are used responsibly and ethically.\n",
            "\n",
            "### 7. Integration with Quantum Computing (Early Stages)\n",
            "\n",
            "While still in its early phases, the integration of quantum computing with LLMs is showing promising results.\n",
            "\n",
            "**Quantum-Enhanced LLMs:** Quantum-enhanced LLMs can process complex data more efficiently and potentially solve problems that are intractable for classical computers. Quantum algorithms can accelerate tasks such as training LLMs and performing inference.\n",
            "\n",
            "**Applications:** This is particularly relevant in areas like cryptography and financial modeling, where complex calculations are required. Quantum-enhanced LLMs could be used to break existing encryption algorithms and develop new, more secure cryptographic systems. They could also be used to develop more accurate financial models.\n",
            "\n",
            "**Challenges:** The integration of quantum computing with LLMs is still in its early stages and faces significant challenges. Quantum computers are still expensive and difficult to build and maintain. Furthermore, developing quantum algorithms for LLMs requires specialized expertise.\n",
            "\n",
            "### 8. Democratization of LLM Development\n",
            "\n",
            "User-friendly platforms and open-source tools have democratized LLM development, allowing individuals and small organizations to create custom models for specific applications.\n",
            "\n",
            "**Low-Code/No-Code Platforms:** Low-code/no-code platforms provide a visual interface for building and deploying LLMs without requiring extensive coding knowledge. These platforms make it easier for non-experts to create custom LLMs.\n",
            "\n",
            "**Open-Source Tools:** Open-source tools provide developers with access to pre-trained LLMs, datasets, and development tools. This allows them to build and customize LLMs for specific applications.\n",
            "\n",
            "**Proliferation of Niche AI Solutions:** This has fostered innovation and led to a proliferation of niche AI solutions tailored to specific industries and use cases. For example, a small business could use a democratized LLM platform to create a custom chatbot for customer service or a tool to analyze market trends.\n",
            "\n",
            "### 9. LLMs for Enhanced Cybersecurity\n",
            "\n",
            "LLMs are being used to proactively identify and mitigate cyber threats.\n",
            "\n",
            "**Anomaly Detection:** These models can analyze network traffic, detect anomalies, and automate incident response. LLMs can learn the normal patterns of network traffic and identify deviations that may indicate a cyberattack.\n",
            "\n",
            "**Threat Intelligence:** LLMs can analyze threat intelligence feeds to identify emerging threats and vulnerabilities. They can also be used to generate reports on cybersecurity trends.\n",
            "\n",
            "**Automated Incident Response:** AI-powered security systems are becoming increasingly sophisticated at adapting to evolving cyberattacks. LLMs can automate incident response by identifying and isolating infected systems, blocking malicious traffic, and alerting security personnel.\n",
            "\n",
            "**Examples:** LLMs are being used to detect phishing emails, malware, and other cyber threats. They are also being used to automate security tasks, such as vulnerability scanning and penetration testing.\n",
            "\n",
            "### 10. LLMs Bridging Language Barriers\n",
            "\n",
            "Real-time, high-fidelity language translation powered by LLMs has broken down communication barriers across the globe.\n",
            "\n",
            "**Seamless Communication:** AI-enabled translation devices and software facilitate seamless interactions in international business, education, and diplomacy. LLMs can translate text and speech in real-time, allowing people from different countries to communicate effectively.\n",
            "\n",
            "**Culturally Sensitive Translation:** Culturally sensitive translation that takes into account nuances and idioms is now standard. LLMs can understand the context and cultural nuances of language to provide more accurate and natural-sounding translations.\n",
            "\n",
            "**Applications:** LLMs are being used in a variety of applications, such as online translation services, video conferencing, and international news broadcasting. They are also being used to translate educational materials and scientific research.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Senior Data Researcher\u001b[00m\n",
            "\u001b[95m## Task:\u001b[00m \u001b[92mConduct a thorough research about AI LLMs Make sure you find any interesting and relevant information given the current year is 2025.\n",
            "\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Senior Data Researcher\u001b[00m\n",
            "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
            "Here are 10 bullet points summarizing the most relevant information about AI LLMs in 2025:\n",
            "\n",
            "*   **Widespread Domain-Specific LLMs:** General-purpose LLMs are now complemented by highly specialized models trained for specific industries (e.g., medicine, law, finance, engineering). These domain-specific models achieve significantly higher accuracy and efficiency within their fields.\n",
            "\n",
            "*   **Advanced Multimodal Integration:** LLMs seamlessly integrate with other modalities like images, video, audio, and sensor data. This enables applications such as autonomous robotics, advanced medical diagnosis based on imaging and text reports, and highly personalized education.\n",
            "\n",
            "*   **Enhanced Explainability and Transparency:** Significant progress has been made in making LLMs more explainable. Techniques like attention visualization, causal reasoning analysis, and counterfactual explanations are routinely used to understand model decisions and biases, fostering trust and accountability.\n",
            "\n",
            "*   **Federated Learning for LLMs:** Federated learning is widely adopted for training LLMs on decentralized data sources while preserving data privacy. This allows organizations to collaborate on model development without sharing sensitive information, leading to more robust and representative models.\n",
            "\n",
            "*   **Neuromorphic Computing Acceleration:** LLMs are increasingly deployed on neuromorphic computing hardware, which mimics the structure and function of the human brain. This results in significant improvements in energy efficiency and processing speed, enabling real-time applications on edge devices.\n",
            "\n",
            "*   **Self-Improving and Continual Learning LLMs:** LLMs can now learn continuously from new data and experiences without forgetting previous knowledge. Self-improvement mechanisms allow models to refine their own architectures and training processes, leading to rapid performance gains.\n",
            "\n",
            "*   **Robustness Against Adversarial Attacks:** Advanced defense mechanisms have been developed to protect LLMs from adversarial attacks, such as prompt injection and data poisoning. These defenses ensure the reliability and security of LLMs in critical applications.\n",
            "\n",
            "*   **Integration with Quantum Computing:** While still in its early stages, quantum computing is beginning to be used to accelerate certain aspects of LLM training and inference. This holds the promise of even more powerful and efficient LLMs in the future.\n",
            "\n",
            "*   **Ethical AI and Bias Mitigation:** Ethical considerations are paramount in LLM development. Frameworks and tools are now commonly used to detect and mitigate biases in training data and model outputs, ensuring fairness and inclusivity. Government regulations are in place regarding LLM deployment.\n",
            "\n",
            "*   **Personalized AI Assistants with Enhanced Emotional Intelligence:** AI assistants powered by LLMs are more personalized and emotionally intelligent than ever before. They can understand and respond to human emotions, providing more empathetic and effective support. They are also capable of adapting to the individual user's preferences and communication style over time.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Reporting Analyst\u001b[00m\n",
            "\u001b[95m## Task:\u001b[00m \u001b[92mReview the context you got and expand each topic into a full section for a report. Make sure the report is detailed and contains any and all relevant information.\n",
            "\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[1m\u001b[95m# Agent:\u001b[00m \u001b[1m\u001b[92mAI LLMs Reporting Analyst\u001b[00m\n",
            "\u001b[95m## Final Answer:\u001b[00m \u001b[92m\n",
            "## AI LLMs in 2025: A Detailed Report\n",
            "\n",
            "### 1. Widespread Domain-Specific LLMs\n",
            "\n",
            "In 2025, the landscape of Large Language Models (LLMs) is characterized by the prevalence of domain-specific models. While general-purpose LLMs continue to exist, the focus has shifted towards creating highly specialized models tailored for specific industries and applications. This specialization arises from the limitations of general-purpose LLMs in achieving optimal accuracy and efficiency across diverse fields.\n",
            "\n",
            "**Key Features of Domain-Specific LLMs:**\n",
            "\n",
            "*   **Targeted Training Data:** These models are trained on curated datasets specific to their intended domain. For example, a medical LLM would be trained on medical literature, patient records (with appropriate anonymization), and clinical trial data. A legal LLM would be trained on legal documents, case law, and regulatory information.\n",
            "*   **Specialized Architectures:** In some cases, the underlying architecture of the LLM is modified to better suit the characteristics of the domain. This might involve incorporating domain-specific knowledge graphs or attention mechanisms that prioritize relevant information within the input data.\n",
            "*   **Fine-Tuning and Transfer Learning:** Domain-specific LLMs are often created through fine-tuning pre-trained general-purpose LLMs on domain-specific data. This transfer learning approach leverages the knowledge already embedded in the general model, accelerating the training process and improving performance.\n",
            "*   **Higher Accuracy and Efficiency:** By focusing on a specific domain, these models achieve significantly higher accuracy and efficiency compared to general-purpose LLMs. They are better at understanding domain-specific terminology, identifying relevant information, and generating accurate and contextually appropriate responses.\n",
            "*   **Examples:**\n",
            "    *   **Medicine:** Assisting with diagnosis, treatment planning, drug discovery, and patient education.\n",
            "    *   **Law:** Conducting legal research, drafting contracts, and analyzing case law.\n",
            "    *   **Finance:** Analyzing market trends, managing risk, and providing financial advice.\n",
            "    *   **Engineering:** Designing structures, optimizing processes, and troubleshooting technical problems.\n",
            "    *   **Education:** Generating personalized learning materials, providing feedback to students, and automating grading.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "The rise of domain-specific LLMs has led to significant advancements in various industries, enabling automation of complex tasks, improved decision-making, and enhanced productivity. They have also democratized access to specialized knowledge, making it easier for individuals and organizations to leverage the power of AI.\n",
            "\n",
            "### 2. Advanced Multimodal Integration\n",
            "\n",
            "LLMs in 2025 demonstrate seamless integration with multiple modalities beyond text, including images, video, audio, and sensor data. This advanced multimodal integration enables a wider range of applications and a more comprehensive understanding of the world.\n",
            "\n",
            "**Key Capabilities:**\n",
            "\n",
            "*   **Cross-Modal Understanding:** LLMs can process and understand information from different modalities simultaneously. For example, they can analyze an image and a corresponding text description to generate a more detailed and accurate understanding of the scene.\n",
            "*   **Multimodal Generation:** LLMs can generate content in multiple modalities. For example, they can generate an image based on a text prompt or create a video with accompanying narration.\n",
            "*   **Modality Translation:** LLMs can translate information from one modality to another. For example, they can transcribe audio into text or generate captions for images.\n",
            "*   **Contextual Awareness:** LLMs can use information from different modalities to improve their contextual awareness. For example, they can use sensor data to understand the environment in which a conversation is taking place.\n",
            "\n",
            "**Applications:**\n",
            "\n",
            "*   **Autonomous Robotics:** LLMs can be used to control robots that operate in complex environments. By integrating information from cameras, sensors, and natural language commands, robots can perform tasks such as navigation, object recognition, and manipulation.\n",
            "*   **Advanced Medical Diagnosis:** LLMs can analyze medical images (e.g., X-rays, MRIs) and text reports to assist doctors in making more accurate diagnoses. They can identify subtle patterns and anomalies that might be missed by human experts.\n",
            "*   **Personalized Education:** LLMs can create personalized learning experiences that adapt to the individual needs of each student. By integrating information from student performance data, learning style preferences, and multimodal inputs, LLMs can provide targeted feedback and support.\n",
            "*   **Human-Computer Interaction:** More natural and intuitive interactions, where users can communicate with systems through a combination of voice, gestures, and text.\n",
            "*   **Content Creation:** LLMs can create richer and more engaging content by combining text, images, and videos.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Multimodal integration has expanded the capabilities of LLMs and unlocked new possibilities across various domains. It has enabled the development of more intelligent and versatile AI systems that can interact with the world in a more natural and human-like way.\n",
            "\n",
            "### 3. Enhanced Explainability and Transparency\n",
            "\n",
            "A major advancement in LLMs by 2025 is the enhanced explainability and transparency of their decision-making processes. Addressing the \"black box\" nature of earlier models is crucial for building trust, ensuring accountability, and enabling effective human oversight.\n",
            "\n",
            "**Techniques for Explainability:**\n",
            "\n",
            "*   **Attention Visualization:** Visualizing the attention weights assigned by the model to different parts of the input sequence. This reveals which words or phrases the model considered most important when making a prediction.\n",
            "*   **Causal Reasoning Analysis:** Analyzing the causal relationships between different variables in the model's input and output. This helps to understand how the model arrives at its conclusions and identify potential biases.\n",
            "*   **Counterfactual Explanations:** Generating alternative scenarios that would have led to different outcomes. This helps to understand the factors that influenced the model's decision and identify potential areas for improvement.\n",
            "*   **Concept Activation Vectors (CAVs):** Identifying and visualizing the concepts that the model has learned. This helps to understand how the model represents knowledge and identify potential biases.\n",
            "*   **Layer-Wise Relevance Propagation (LRP):** Tracing the decision-making process of the model back through its layers to identify the most relevant input features.\n",
            "\n",
            "**Benefits of Explainability:**\n",
            "\n",
            "*   **Increased Trust:** Understanding how LLMs make decisions fosters trust among users and stakeholders.\n",
            "*   **Improved Accountability:** Explainability allows for identifying and addressing biases and errors in model predictions, leading to more accountable AI systems.\n",
            "*   **Effective Human Oversight:** Explainable models enable humans to effectively monitor and intervene in the decision-making process, ensuring that AI systems are aligned with human values.\n",
            "*   **Enhanced Debugging and Improvement:** Understanding the model's reasoning process facilitates debugging and identifying areas for improvement.\n",
            "*   **Regulatory Compliance:** Many regulations require AI systems to be transparent and explainable, particularly in high-stakes applications.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Enhanced explainability and transparency have been crucial for the widespread adoption of LLMs in critical applications. By making these models more understandable and trustworthy, developers and users can ensure that they are used responsibly and ethically.\n",
            "\n",
            "### 4. Federated Learning for LLMs\n",
            "\n",
            "Federated learning has become a widely adopted approach for training LLMs in 2025, particularly in scenarios where data privacy is a major concern. This technique allows organizations to collaboratively train models on decentralized data sources without sharing sensitive information directly.\n",
            "\n",
            "**How Federated Learning Works:**\n",
            "\n",
            "1.  **Model Initialization:** A central server initializes a global LLM.\n",
            "2.  **Model Distribution:** The server distributes a copy of the model to multiple participating clients (e.g., hospitals, banks, research institutions).\n",
            "3.  **Local Training:** Each client trains the model on its local dataset. The data remains on the client's device or server and is not shared with the central server or other clients.\n",
            "4.  **Model Aggregation:** After training, each client sends the updated model parameters (e.g., weights and biases) back to the central server.\n",
            "5.  **Global Model Update:** The server aggregates the updates from all clients to create a new, improved global model. This aggregation process can involve averaging the updates or using more sophisticated techniques to account for differences in data quality and quantity.\n",
            "6.  **Iteration:** Steps 2-5 are repeated iteratively until the global model converges to a desired level of performance.\n",
            "\n",
            "**Benefits of Federated Learning:**\n",
            "\n",
            "*   **Data Privacy:** Sensitive data remains on the client's device or server, reducing the risk of data breaches and privacy violations.\n",
            "*   **Collaboration:** Organizations can collaborate on model development without sharing proprietary data, leading to more robust and representative models.\n",
            "*   **Reduced Communication Costs:** Only model updates are exchanged, reducing the bandwidth requirements compared to traditional centralized training.\n",
            "*   **Personalization:** Federated learning can be used to create personalized models that are tailored to the specific needs of individual users or organizations.\n",
            "*   **Compliance:** Federated learning can help organizations comply with data privacy regulations such as GDPR and CCPA.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "*   **Communication Costs:** Uploading model updates can still be bandwidth intensive, particularly with large models.\n",
            "*   **Heterogeneity:** Data quality and quantity can vary significantly across different clients, which can impact model performance.\n",
            "*   **Security:** Federated learning systems are still vulnerable to attacks such as model poisoning and backdoor attacks.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Federated learning has enabled the development of more robust and representative LLMs while preserving data privacy. This has been particularly important in industries such as healthcare, finance, and government, where data privacy is paramount.\n",
            "\n",
            "### 5. Neuromorphic Computing Acceleration\n",
            "\n",
            "In 2025, LLMs are increasingly deployed on neuromorphic computing hardware, which is inspired by the structure and function of the human brain. This specialized hardware offers significant advantages in terms of energy efficiency and processing speed.\n",
            "\n",
            "**Key Features of Neuromorphic Computing:**\n",
            "\n",
            "*   **Spiking Neural Networks (SNNs):** Neuromorphic chips typically use spiking neural networks, which mimic the way neurons communicate in the brain. Instead of transmitting continuous values, neurons in SNNs transmit discrete spikes.\n",
            "*   **Event-Driven Processing:** Neuromorphic chips process information only when there is a change in the input signal. This event-driven processing reduces energy consumption compared to traditional von Neumann architectures.\n",
            "*   **Parallel Processing:** Neuromorphic chips are highly parallel, allowing them to process information from multiple sources simultaneously.\n",
            "*   **Low Power Consumption:** Neuromorphic chips consume significantly less power than traditional processors, making them ideal for edge computing applications.\n",
            "\n",
            "**Benefits for LLMs:**\n",
            "\n",
            "*   **Energy Efficiency:** Neuromorphic computing can significantly reduce the energy consumption of LLMs, making them more sustainable and cost-effective to deploy.\n",
            "*   **Processing Speed:** Neuromorphic chips can accelerate certain aspects of LLM training and inference, enabling real-time applications.\n",
            "*   **Edge Computing:** The low power consumption and high processing speed of neuromorphic chips make them ideal for deploying LLMs on edge devices such as smartphones, wearable devices, and autonomous vehicles.\n",
            "*   **Real-time Applications:** The increased processing speed allows for real-time analysis of data streams, important for applications like live translation or autonomous driving.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "*   **Maturity:** Neuromorphic computing is still a relatively new field, and the hardware and software tools are not as mature as those for traditional processors.\n",
            "*   **Programming Complexity:** Programming neuromorphic chips can be more challenging than programming traditional processors.\n",
            "*   **Model Compatibility:** Not all LLM architectures are well-suited for neuromorphic computing.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Neuromorphic computing has the potential to revolutionize the deployment of LLMs, making them more energy-efficient, faster, and more accessible. This technology is particularly promising for edge computing applications, where power consumption and latency are critical constraints.\n",
            "\n",
            "### 6. Self-Improving and Continual Learning LLMs\n",
            "\n",
            "LLMs in 2025 are increasingly capable of self-improvement and continual learning. This means they can learn continuously from new data and experiences without forgetting previous knowledge.\n",
            "\n",
            "**Key Mechanisms:**\n",
            "\n",
            "*   **Reinforcement Learning:** LLMs can use reinforcement learning to optimize their own architectures and training processes. For example, they can learn to select the best hyperparameters for a given task or to design new neural network architectures.\n",
            "*   **Meta-Learning:** LLMs can use meta-learning to learn how to learn more effectively. This allows them to adapt quickly to new tasks and domains.\n",
            "*   **Lifelong Learning:** LLMs can use lifelong learning techniques to retain knowledge gained from previous tasks while learning new tasks. This prevents catastrophic forgetting, a common problem in traditional machine learning.\n",
            "*   **Automated Data Curation:** Systems that can automatically identify, filter, and incorporate new, relevant data into the LLM's training dataset.\n",
            "*   **Self-Supervised Learning:** Ability to learn from unlabeled data, allowing the LLM to constantly expand its knowledge base without explicit human intervention.\n",
            "\n",
            "**Benefits:**\n",
            "\n",
            "*   **Rapid Performance Gains:** Self-improvement mechanisms allow models to refine their own architectures and training processes, leading to rapid performance gains.\n",
            "*   **Adaptability:** Continual learning allows models to adapt to changing environments and new tasks.\n",
            "*   **Reduced Training Costs:** Self-improving models can reduce the need for human intervention in the training process, lowering training costs.\n",
            "*   **Knowledge Retention:** Continual learning prevents catastrophic forgetting, ensuring that models retain knowledge gained from previous tasks.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "*   **Stability:** Self-improvement mechanisms can sometimes lead to instability, causing models to diverge from their intended behavior.\n",
            "*   **Bias Amplification:** Continual learning can amplify biases in the training data, leading to unfair or discriminatory outcomes.\n",
            "*   **Computational Resources:** Training self-improving and continual learning models can be computationally expensive.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Self-improving and continual learning LLMs have the potential to revolutionize the field of AI. By enabling models to learn and adapt continuously, these techniques can create AI systems that are more intelligent, versatile, and robust.\n",
            "\n",
            "### 7. Robustness Against Adversarial Attacks\n",
            "\n",
            "The year 2025 sees the implementation of advanced defense mechanisms to protect LLMs from adversarial attacks. These attacks, such as prompt injection and data poisoning, can compromise the reliability and security of LLMs, making robust defenses crucial for their deployment in critical applications.\n",
            "\n",
            "**Types of Adversarial Attacks:**\n",
            "\n",
            "*   **Prompt Injection:** Attackers craft malicious prompts that can manipulate the LLM's behavior, causing it to generate incorrect, harmful, or biased outputs.\n",
            "*   **Data Poisoning:** Attackers inject malicious data into the training dataset, causing the LLM to learn incorrect or biased patterns.\n",
            "*   **Evasion Attacks:** Attackers modify the input data in subtle ways to cause the LLM to misclassify or misinterpret it.\n",
            "*   **Model Extraction:** Attackers attempt to steal the LLM's parameters or architecture.\n",
            "\n",
            "**Defense Mechanisms:**\n",
            "\n",
            "*   **Input Validation:** Carefully validating the input prompt to detect and remove malicious code or instructions.\n",
            "*   **Adversarial Training:** Training the LLM on adversarial examples to make it more robust to attacks.\n",
            "*   **Regularization Techniques:** Using regularization techniques to prevent the LLM from overfitting to the training data.\n",
            "*   **Anomaly Detection:** Detecting anomalous behavior in the LLM's outputs.\n",
            "*   **Watermarking:** Embedding a watermark into the LLM's outputs to detect whether they have been tampered with.\n",
            "*   **Sandboxing:** Running the LLM in a sandboxed environment to prevent it from accessing sensitive resources.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Robust defense mechanisms are essential for ensuring the reliability and security of LLMs in critical applications. By protecting these models from adversarial attacks, developers can ensure that they are used responsibly and ethically.\n",
            "\n",
            "### 8. Integration with Quantum Computing\n",
            "\n",
            "While still in its early stages in 2025, quantum computing is beginning to be used to accelerate certain aspects of LLM training and inference. This nascent integration holds the promise of even more powerful and efficient LLMs in the future.\n",
            "\n",
            "**Potential Applications of Quantum Computing for LLMs:**\n",
            "\n",
            "*   **Accelerated Training:** Quantum algorithms can potentially speed up the training process of LLMs, particularly for large and complex models.\n",
            "*   **Improved Optimization:** Quantum optimization algorithms can be used to find better sets of parameters for LLMs, leading to improved performance.\n",
            "*   **Enhanced Feature Extraction:** Quantum machine learning techniques can be used to extract more informative features from the training data.\n",
            "*   **Novel Architectures:** Quantum computing may enable the development of entirely new LLM architectures that are not possible with classical computers.\n",
            "*   **Quantum-Resistant Security:** Developing LLMs and security protocols that are resistant to attacks from quantum computers.\n",
            "\n",
            "**Challenges:**\n",
            "\n",
            "*   **Hardware Limitations:** Quantum computers are still in their early stages of development and are not yet powerful enough to handle the full complexity of LLMs.\n",
            "*   **Algorithm Development:** Quantum algorithms for machine learning are still being developed and are not as mature as classical algorithms.\n",
            "*   **Integration Complexity:** Integrating quantum computers with classical computing infrastructure can be challenging.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Although the integration of quantum computing with LLMs is still in its early stages, it has the potential to significantly accelerate the development of more powerful and efficient AI systems. As quantum computing technology matures, it is likely to play an increasingly important role in the field of LLMs.\n",
            "\n",
            "### 9. Ethical AI and Bias Mitigation\n",
            "\n",
            "Ethical considerations are paramount in LLM development by 2025. Frameworks and tools are commonly used to detect and mitigate biases in training data and model outputs, ensuring fairness and inclusivity. Government regulations are in place regarding LLM deployment to ensure responsible and ethical use.\n",
            "\n",
            "**Key Considerations:**\n",
            "\n",
            "*   **Bias Detection:** Using tools and techniques to identify biases in training data and model outputs.\n",
            "*   **Bias Mitigation:** Implementing strategies to mitigate biases, such as data augmentation, re-weighting, and adversarial training.\n",
            "*   **Fairness Metrics:** Evaluating LLMs using fairness metrics to ensure that they do not discriminate against certain groups of people.\n",
            "*   **Transparency and Explainability:** Making LLMs more transparent and explainable to understand how they make decisions and identify potential biases.\n",
            "*   **Accountability:** Establishing clear lines of accountability for the development and deployment of LLMs.\n",
            "*   **Data Privacy:** Protecting the privacy of individuals whose data is used to train LLMs.\n",
            "*   **Security:** Protecting LLMs from adversarial attacks that could compromise their ethical behavior.\n",
            "\n",
            "**Government Regulations:**\n",
            "\n",
            "Governments around the world have implemented regulations regarding the development and deployment of LLMs. These regulations typically focus on issues such as data privacy, bias, transparency, and accountability.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Ethical AI and bias mitigation are essential for ensuring that LLMs are used responsibly and ethically. By addressing these considerations, developers can create AI systems that are fair, inclusive, and beneficial to society.\n",
            "\n",
            "### 10. Personalized AI Assistants with Enhanced Emotional Intelligence\n",
            "\n",
            "AI assistants powered by LLMs in 2025 are more personalized and emotionally intelligent than ever before. They understand and respond to human emotions, providing more empathetic and effective support.\n",
            "\n",
            "**Key Features:**\n",
            "\n",
            "*   **Emotion Recognition:** AI assistants can recognize human emotions from facial expressions, voice tone, and text input.\n",
            "*   **Emotion Understanding:** AI assistants can understand the context of human emotions and respond appropriately.\n",
            "*   **Personalized Responses:** AI assistants can tailor their responses to the individual user's preferences and communication style.\n",
            "*   **Adaptive Learning:** AI assistants can learn from user interactions and improve their ability to provide personalized and emotionally intelligent support over time.\n",
            "*   **Proactive Assistance:** AI assistants can anticipate user needs and provide proactive assistance.\n",
            "\n",
            "**Benefits:**\n",
            "\n",
            "*   **Improved User Experience:** Personalized and emotionally intelligent AI assistants provide a more natural and engaging user experience.\n",
            "*   **Enhanced Productivity:** AI assistants can help users to be more productive by automating tasks and providing personalized support.\n",
            "*   **Better Mental Health:** AI assistants can provide emotional support and companionship, which can improve users' mental health.\n",
            "*   **Increased Accessibility:** AI assistants can make technology more accessible to people with disabilities.\n",
            "\n",
            "**Impact:**\n",
            "\n",
            "Personalized AI assistants with enhanced emotional intelligence have the potential to transform the way we interact with technology. These assistants can provide more natural, engaging, and effective support, improving our productivity, mental health, and overall quality of life.\u001b[00m\n",
            "\n",
            "\n",
            "\u001b[3m                                      Tasks Scores                                       \u001b[0m\n",
            "\u001b[3m                                 (1-10 Higher is better)                                 \u001b[0m\n",
            "┏━━━━━━━━━━━━━━━━━━━━┯━━━━━━━┯━━━━━━━┯━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━┓\n",
            "┃\u001b[1m \u001b[0m\u001b[1mTasks/Crew/Agents \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mRun 1\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mRun 2\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mAvg. Total\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1mAgents                          \u001b[0m\u001b[1m \u001b[0m│\u001b[1m  \u001b[0m┃\n",
            "┠────────────────────┼───────┼───────┼────────────┼──────────────────────────────────┼──┨\n",
            "┃\u001b[36m \u001b[0m\u001b[36mTask 1            \u001b[0m\u001b[36m \u001b[0m│  9.5  │  9.5  │    9.5     │\u001b[32m \u001b[0m\u001b[32m- AI LLMs Senior Data Researcher\u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m                    \u001b[0m│       │       │            │\u001b[32m \u001b[0m\u001b[32m                                \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m \u001b[0m\u001b[36m                  \u001b[0m\u001b[36m \u001b[0m│       │       │            │\u001b[32m \u001b[0m\u001b[32m                                \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m \u001b[0m\u001b[36mTask 2            \u001b[0m\u001b[36m \u001b[0m│  9.5  │  9.0  │    9.2     │\u001b[32m \u001b[0m\u001b[32m- AI LLMs Reporting Analyst     \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m                    \u001b[0m│       │       │            │\u001b[32m \u001b[0m\u001b[32m                                \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m \u001b[0m\u001b[36mCrew              \u001b[0m\u001b[36m \u001b[0m│ 9.50  │ 9.25  │    9.4     │\u001b[32m \u001b[0m\u001b[32m                                \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┃\u001b[36m \u001b[0m\u001b[36mExecution Time (s)\u001b[0m\u001b[36m \u001b[0m│  22   │  41   │     31     │\u001b[32m \u001b[0m\u001b[32m                                \u001b[0m\u001b[32m \u001b[0m│  ┃\n",
            "┗━━━━━━━━━━━━━━━━━━━━┷━━━━━━━┷━━━━━━━┷━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━┛\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task: Run it with Flows. Find the way for it.**"
      ],
      "metadata": {
        "id": "Tgz-7b3vk10I"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bSzTx93Xk6Wb"
      },
      "execution_count": null,
      "outputs": []
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}